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EnglishConvolutional neural networks (CNNs) are fueling the advancement of autonomous palm-sized drones, i.e., nano-drones, despite their limited power envelope and onboard processing capabilities. Computationally lighter than traditional geometrical approaches, CNNs are the ideal candidates to predict end-to-end signals directly from the sensor inputs to feed to the onboard flight controller. However, these sophisticated CNNs require significant complexity reduction and fine-grained tuning to be successfully deployed aboard a flying nano-drone. To date, these optimizations are mostly hand-crafted and require error-prone, labor-intensive iterative development flows. This work discusses methodologies and software tools to streamline and automate all the deployment stages on a low-power commercial multicore System-on-Chip. We investigate both an industrial closed-source and an academic open-source tool-set with a field-proofed state-of-the-art CNN for autonomous driving. Our results show a 2 reduction of the memory footprint and a speedup of 1.6 in the inference time, compared to the original hand-crafted CNN, with the same prediction accuracy.
| Titolo: | Automated Tuning of End-to-end Neural Flight Controllers for Autonomous Nano-drones | |
| Autore/i: | Niculescu V.; Lamberti L.; Palossi D.; Benini L. | |
| Autore/i Unibo: | ||
| Anno: | 2021 | |
| Titolo del libro: | 2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems, AICAS 2021 | |
| Pagina iniziale: | 1 | |
| Pagina finale: | 4 | |
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1109/AICAS51828.2021.9458550 | |
| Abstract: | Convolutional neural networks (CNNs) are fueling the advancement of autonomous palm-sized drones, i.e., nano-drones, despite their limited power envelope and onboard processing capabilities. Computationally lighter than traditional geometrical approaches, CNNs are the ideal candidates to predict end-to-end signals directly from the sensor inputs to feed to the onboard flight controller. However, these sophisticated CNNs require significant complexity reduction and fine-grained tuning to be successfully deployed aboard a flying nano-drone. To date, these optimizations are mostly hand-crafted and require error-prone, labor-intensive iterative development flows. This work discusses methodologies and software tools to streamline and automate all the deployment stages on a low-power commercial multicore System-on-Chip. We investigate both an industrial closed-source and an academic open-source tool-set with a field-proofed state-of-the-art CNN for autonomous driving. Our results show a 2 reduction of the memory footprint and a speedup of 1.6 in the inference time, compared to the original hand-crafted CNN, with the same prediction accuracy. | |
| Data stato definitivo: | 26-feb-2022 | |
| Appare nelle tipologie: | 4.01 Contributo in Atti di convegno |
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